Semantic distance is a way to operationalize semantic discriminability in the case where there are \(n=2\)  features and concepts [1]}. Fig. REF  illustrates an example in which we have concepts {M,W} and colors {1,2}. The color-concept associations between all possible pairs are \(x_1,\dots , x_4\) , as shown in Fig. REF . We assume each \(x_k\)  is normally distributed with mean \(\bar{x}_k\)  equal to the corresponding \(a_{ij}\)  and standard deviation \(\sigma _k = 1.4\cdot \bar{x}_k(1-\bar{x}_k)\) , which was found to be a good fit to experimental data [1]}. The outcome of the assignment problem is determined by the quantity \(\Delta x := x_1-x_2+x_3-x_4\) . The optimal assignment is:
(M-1 and W-2 if \(\Delta x > 0\) ) and (M-2 and W-1 if \(\Delta x < 0\) ).
